Publication in BibTeX Format

@INBOOK{AICPub1583:2007,
AUTHOR={Rosenfeld, A. and Kraus, S. and Ortiz, C.},
TITLE={ Quantifying the Expected Utility of Information in Multi-agent Scheduling
Tasks},
BOOKTITLE={Cooperative Information Agents XI},
ISBN={978-3-540-75118-2},
PUBLISHER={Springer},
SERIES={Lecture Notes in Computer Science},
PAGES={104-118},
VOLUME={4676},
YEAR={2007},
COPYRIGHT={2007},
ABSTRACT={In this paper we investigate methods for analyzing the expected value
of adding information in distributed task scheduling problems. As scheduling
problems are NP-complete, no polynomial algorithms exist for evaluating the
impact a certain constraint, or relaxing the same constraint, will have on
the global problem. We present a general approach where local agents can estimate
their problem tightness, or how constrained their local subproblem is. This
allows these agents to immediately identify many problems which are not constrained,
and will not benefit from sending or receiving further information. Next, agents
use traditional machine learning methods based on their specific local problem
attributes to attempt to identify which of the constrained problems will most
benefit from human attention. We evaluated this approach within a distributed
cTAEMS scheduling domain and found this approach was overall quite effective.}
}